scholarly journals Variomes: a high recall search engine to support the curation of genomic variants

2021 ◽  
Author(s):  
Emilie Pasche ◽  
Anaïs Mottaz ◽  
Deborah Caucheteur ◽  
Julien Gobeill ◽  
Pierre-André Michel ◽  
...  
Keyword(s):  
Search Engine ◽  
Genetic Variants ◽  
Variant Calling ◽  
Tumor Board ◽  
Precision Oncology ◽  
Large Set ◽  
Experimental Setting ◽  
Competitive System ◽  
Retrieval Task ◽  
Actionable Variants

Precision oncology relies on the use of treatments targeting specific genetic variants. However, identifying clinically actionable variants as well as relevant information likely to be used to treat a patient with a given cancer is a labor-intensive task, which includes searching the literature for a large set of variants. The lack of universally adopted standard nomenclature for variants requires the development of variant-specific literature search engines. We develop a system to perform triage of publications relevant to support an evidence-based decision. Together with providing a ranked list of articles for a given variant, the system is also able to prioritize variants, as found in a Variant Calling Format, assuming that the clinical actionability of a genetic variant is correlated with the volume of literature published about the variant. Our system searches within three pre-annotated document collections: MEDLINE abstracts, PubMed Central full-text articles and ClinicalTrials.gov clinical trials. A variant synonym generator is used to increase the comprehensiveness of the set of retrieved documents. We then apply different strategies to rank the publications. We assess the search effectiveness of the system using different experimental settings. Experimental setting 1: The literature retrieval task is tuned and evaluated using the TREC Precision Medicine 2018 and 2019 benchmarks consisting respectively in 50 and 40 topics. Almost two thirds (62%) of the publications returned in the top-5 are relevant for clinical decision-support. Experimental setting 2: The evaluation of the variant prioritization task is based on a manually-created benchmark composed of eight patients for a total of 756 variants. For each patient, we used both their complete set of variants and tumor board reports. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Experimental setting 3: A comparison of Variomes with LitVar, a well-known search engine for genetic variants is performed. Variomes was able to retrieve on average 90.8% of the content, while LitVar retrieved on average 58.6%. Out of the 9.2% articles, which are "missed" by Variomes, a per error analysis suggests that they are artefacts. To conclude, we are proposing here a competitive system to facilitate the curation of variants for personalized medicine.

scholarly journals DEEPGENTM—A Novel Variant Calling Assay for Low Frequency Variants

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Genetic Variants ◽  
Liquid Biopsy ◽  
Hot Spot ◽  
Treatment Success ◽  
Low Frequency ◽  
Variant Calling ◽  
Subsequent Treatment ◽  
Precision Oncology ◽  
Orthogonal Comparison

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

scholarly journals scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gavin W. Wilson ◽  
Mathieu Derouet ◽  
Gail E. Darling ◽  
Jonathan C. Yeung
Keyword(s):  
Genetic Variants ◽  
False Positive ◽  
Variant Calling ◽  
Call Rate ◽  
Rna Seq ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Variant Call ◽  
Two Samples ◽  
Co Detection

AbstractIdentifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to “collapse” molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples.

Impact of Molecular Tumor Board (MTB) on precision oncology in a community setting

2021 ◽  
Vol 162 ◽  
pp. S180
Author(s):  
Adam ElNaggar ◽  
Gregory Vidal ◽  
Ari VanderWalde ◽  
Lee Schwartzberg ◽  
Axel Grothey ◽  
...  
Keyword(s):  
Community Setting ◽  
Tumor Board ◽  
Precision Oncology ◽  
Molecular Tumor Board

scholarly journals CATCH: A Prospective Precision Oncology Trial in Metastatic Breast Cancer

2021 ◽  
pp. 676-686
Author(s):  
Mario Hlevnjak ◽  
Markus Schulze ◽  
Shaymaa Elgaafary ◽  
Carlo Fremd ◽  
Laura Michel ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Clinical Management ◽  
Metastatic Breast ◽  
Previous Treatment ◽  
Progression Free Survival ◽  
Tumor Board ◽  
Precision Oncology ◽  
Whole Genome ◽  
Free Survival

PURPOSE CATCH (Comprehensive Assessment of clinical feaTures and biomarkers to identify patients with advanced or metastatic breast Cancer for marker driven trials in Humans) is a prospective precision oncology program that uses genomics and transcriptomics to guide therapeutic decisions in the clinical management of metastatic breast cancer. Herein, we report our single-center experience and results on the basis of the first 200 enrolled patients of an ongoing trial. METHODS From June 2017 to March 2019, 200 patients who had either primary metastatic or progressive disease, with any number of previous treatment lines and at least one metastatic site accessible to biopsy, were enrolled. DNA and RNA from tumor tissue and corresponding blood-derived nontumor DNA were profiled using whole-genome and transcriptome sequencing. Identified actionable alterations were brought into clinical context in a multidisciplinary molecular tumor board (MTB) with the aim of prioritizing personalized treatment recommendations. RESULTS Among the first 200 enrolled patients, 128 (64%) were discussed in the MTB, of which 64 (50%) were subsequently treated according to MTB recommendation. Of 53 evaluable patients, 21 (40%) achieved either stable disease (n = 13, 25%) or partial response (n = 8, 15%). Furthermore, 16 (30%) of those patients showed improvement in progression-free survival of at least 30% while on MTB-recommended treatment compared with the progression-free survival of the previous treatment line. CONCLUSION The initial phase of this study demonstrates that precision oncology on the basis of whole-genome and RNA sequencing is feasible when applied in the clinical management of patients with metastatic breast cancer and provides clinical benefit to a substantial proportion of patients.

The Johns Hopkins Molecular Tumor Board Precision Oncology elective for Medical Oncology fellows.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11035-11035
Author(s):  
Kristen Marrone ◽  
Jessica Tao ◽  
Jenna VanLiere Canzoniero ◽  
Paola Ghanem ◽  
Emily Nizialek ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Cancer Genomics ◽  
Medical Oncology ◽  
Clinical Decision ◽  
Tumor Board ◽  
Precision Oncology ◽  
Adaptive Expertise ◽  
Molecular Tumor Board

11035 Background: The accelerated impact of next generation sequencing (NGS) in clinical decision making requires the integration of cancer genomics and precision oncology focused training into medical oncology education. The Johns Hopkins Molecular Tumor Board (JH MTB) is a multi-disciplinary effort focused on integration of NGS findings with critical evidence interpretation to generate personalized recommendations tailored to the genetic footprint of individual patients. Methods: The JH MTB and the Medical Oncology Fellowship Program have developed a 3-month precision oncology elective for fellows in their research years. Commencing fall of 2020, the goals of this elective are to enhance the understanding of NGS platforms and findings, advance the interpretation and characterization of molecular assay outputs by use of mutation annotators and knowledgebases and ultimately master the art of matching NGS findings with available therapies. Fellow integration into the MTB focuses on mentored case-based learning in mutation characterization and ranking by levels of evidence for actionability, with culmination in form of verbal presentations and written summary reports of final MTB recommendations. A mixed methods questionnaire was administered to evaluate progress since elective initiation. Results: Three learners who have participated as of February 2021 were included. Of the two who had completed the MTB elective, each have presented at least 10 cases, with at least 1 scholarly publication planned. All indicated strong agreement that MTB elective had increased their comfort with interpreting clinical NGS reports as well as the use of knowledgebases and variant annotators. Exposure to experts in the field of molecular precision oncology, identification of resources necessary to interpret clinical NGS reports, development of ability to critically assess various NGS platforms, and gained familiarity with computational analyses relevant to clinical decision making were noted as strengths of the MTB elective. Areas of improvement included ongoing initiatives that involve streamlining variant annotation and transcription of information for written reports. Conclusions: A longitudinal elective in the JHU MTB has been found to be preliminarily effective in promoting knowledge mastery and creating academic opportunities related to the clinical application of precision medicine. Future directions will include leveraging of the MTB infrastructure for research projects, learner integration into computational laboratory meetings, and expansion of the MTB curriculum to include different levels of learners from multiple medical education programs. Continued elective participation will be key to understanding how best to facilitate adaptive expertise in assigning clinical relevance to genomic findings, ultimately improving precision medicine delivery in patient care and trial development.

scholarly journals FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyunbin Kim ◽  
Andy Jinseok Lee ◽  
Jongkeun Lee ◽  
Hyonho Chun ◽  
Young Seok Ju ◽  
...  
Keyword(s):  
Data Storage ◽  
Human Cancer ◽  
Point Mutations ◽  
Variant Calling ◽  
Human Tumor ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Mutational Signatures ◽  
Accurate Identification

Abstract Background Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. Results Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. Conclusions In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

scholarly journals Comparison of Treatment Recommendations by Molecular Tumor Boards Worldwide

2018 ◽  
pp. 1-14 ◽  
Author(s):  
Damian T. Rieke ◽  
Mario Lamping ◽  
Marissa Schuh ◽  
Christophe Le Tourneau ◽  
Neus Basté ◽  
...  
Keyword(s):  
Treatment Strategies ◽  
Treatment Recommendations ◽  
Tumor Board ◽  
Precision Oncology ◽  
Genomic Information ◽  
Specific Patient ◽  
Rational Decision Making ◽  
Copy Numbers ◽  
Tumor Boards ◽  
Molecular Profiles

Purpose Precision oncology holds the promise of improving patient outcome. It is based on the idea that the testing of genomic biomarkers can lead to the recommendation of a treatment option tailored to the specific patient. To derive treatment recommendations from molecular profiles, interdisciplinary molecular tumor boards (MTBs) have been established recently in many academic institutions. The recommendation process in MTBs, however, has not been well defined, which limits applicability to larger clinical trials and patient populations. Methods We created four fictional patients on the basis of recent real cases with genomic information on mutations, fusions, copy numbers, and gene expression. We identified 29 tumor boards from nine countries worldwide and asked them to provide treatment recommendations for the sample patients. In addition, a questionnaire regarding the setup and methods used by MTBs was circulated. Results Five MTBs from four countries provided treatment recommendations and answered the questionnaire. For one patient, three tumor board treatment recommendations were identical, and two tumor boards had identical treatment strategies for the other three patients. There was heterogeneity in the interpretation of tumor and germline aberrations as well as in standards of prioritization. Conclusion Differences in the interpretation and recommendation process contribute to heterogeneity in MTB recommendations. Additional comparative analyses of recommendations could help improve rational decision making and lead to standardization.

scholarly journals Comparative Analysis of Public Knowledge Bases for Precision Oncology

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Steffen Pallarz ◽  
Manuela Benary ◽  
Mario Lamping ◽  
Damian Rieke ◽  
Johannes Starlinger ◽  
...  
Keyword(s):  
Genetic Alterations ◽  
Knowledge Bases ◽  
Tumor Board ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Accurate Information ◽  
Precision Oncology ◽  
Cancer Genes ◽  
Qualitative Comparison ◽  
Data Set

PURPOSE Precision oncology depends on the availability of up-to-date, comprehensive, and accurate information about associations between genetic variants and therapeutic options. Recently, a number of knowledge bases (KBs) have been developed that gather such information on the basis of expert curation of the scientific literature. We performed a quantitative and qualitative comparison of Clinical Interpretations of Variants in Cancer, OncoKB, Cancer Gene Census, Database of Curated Mutations, CGI Biomarkers (the cancer genome interpreter biomarker database), Tumor Alterations Relevant for Genomics-Driven Therapy, and the Precision Medicine Knowledge Base. METHODS We downloaded each KB and restructured their content to describe variants, genes, drugs, and gene-drug associations in a common format. We normalized gene names to Entrez Gene IDs and drug names to ChEMBL and DrugBank IDs. For the analysis of clinically relevant gene-drug associations, we obtained lists of genes affected by genetic alterations and putative drug therapies for 113 patients with cancer whose cases were presented at the Molecular Tumor Board (MTB) of the Charité Comprehensive Cancer Center. RESULTS Our analysis revealed that the KBs are largely overlapping but also that each source harbors a notable amount of unique information. Although some KBs cover more genes, others contain more data about gene-drug associations. Retrospective comparisons with findings of the Charitè MTB at the gene level showed that use of multiple KBs may considerably improve retrieval results. The relative importance of a KB in terms of cancer genes was assessed in more detail by logistic regression, which revealed that all but one source had a notable impact on result quality. We confirmed these findings using a second data set obtained from an independent MTB. CONCLUSION To date, none of the existing publicly available KBs on gene-drug associations in precision oncology fully subsumes the others, but all of them exhibit specific strengths and weaknesses. Consideration of multiple KBs, therefore, is essential to obtain comprehensive results.

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